Least squares support vector machine

Huanxiang L, Xiaojun Y, Ruisheng Zh, Mancang L, Zhide H, Botao F (2005) Accurate quantitative structure-property relationship model to predict the solubility of C60 in various solvents based on a novel approach using a least-squares support vector machine. J. Phys. Chem. Sect B. 109 20565-20571. 

U. Thissen, B. Ustun, W.J. Meissen and L.M.C. Buydens, Multivariate calibration with least-squares support vector machines, AwaZ. Chem., 76, 3099 (2004). 

Yao X, Liu H, Zhang R, Liu M, Hu Z, Panaye A, et al. QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines. Mol Pharm 2005 2 348-56. 

JA Suykens, TV Gestel, J de Brabanter, B De Moor, and J Van-derwalle. Least Squares Support Vector Machines. World Scientific Publishing Co., Singapore, 2002. 

T Van Gestel, J Suykens, G Lanckriet, A Lambrechts, B De Moor, and J Vandewalle. Bayesian framework for least squares support vector machine classifiers, Gaussian processes, and kernel Fisher discriminant analysis. Neural Computation, 15 1115-1148, 2002. 

Li, J., Liu, H Yao, X.-J., Liu, M., Hu, Z. and Fan, B.T. (2007) Quantitative structure-activity relationship study of acyl ureas as inhibitors of human liver glycogen phosphorylase using least squares support vector machines. Chemom. Intell. Lab. Syst., 87, 139-146. 

Suykens, J.A.K., Van Gestel, T., De Brabanter, J, De Moor, B., and Vandewalle, J. (2002) Least Squares Support Vector Machines, World Scientific, Singapore. 

Vong, C. M., Wong, P. K. and Li, Y. P. Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference. International Journal of Engineering Application of Artificial Intelligence 19(3) (2006), 227-297. 

X. Wang, J. Chang, K. Wang, M. Ye, Least square support vector machines in combination with principal component analysis for electroiric nose data classification, in Proceedings of 2nd International Symposium on Information Science and Engineering (Shanghai, China, 2009), pp. 348-352... 

Gao T, Sun S-L, Shi L-L et al (2009) An accurate density functional theory calculation for electronic excitation energies the least-squares support vector machine. J Chem Phys 130 184104. doi 10.1063/1.3126773... 

Lipscomb JC, Fisher JW, Confer PD, Byczkowski JZ (1998) In vitro to in vivo extrapolation for trichloroethylene metabolism in humans. Toxicol Appl Pharmacol 152 376-387 Liu HX, Yao XJ, Zhang RS, Liu MC, Hu ZD, Fan BT (2005a) Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines. J Comput Aided Mol Des 19 499-508... 

LIBSVM An SVM library with a graphical interface —LOOMS A leave-one-out model selection for SVM — Least Squares - Support Vector Machines MATLAB /C Toolbox... 

Chauchard, F. et aL (2005) Least-squares support vector machines modelization for time-resolved spectroscopy. Appl. Opt., 44 (33), 7091-7097. 

LS-SVMlab, http //www.esat.kuleuven.ac.be/sista/lssvmlab/. LS-SVMlab, by Suykens, is a MATLAB implementation of least-squares support vector machines (LS-SVMs), a reformulation of the standard SVM that leads to solving linear KKT systems. LS-SVM primal-dual formulations have been formulated for kernel PCA, kernel CCA, and kernel PLS, thereby extending the class of primal-dual kernel machines. Links between kernel versions of classic pattern recognition algorithms such as kernel Fisher discriminant analysis and extensions to unsupervised learning, recurrent networks, and control are available. 

Multivariate Calibration with Least-Squares Support Vector Machines. 

Key Words 2D-QSAR traditional QSAR 3D-QSAR nD-QSAR 4D-QSAR receptor-independent QSAR receptor-dependent QSAR high throughput screening alignment conformation chemometrics principal components analysis partial least squares artificial neural networks support vector machines Binary-QSAR selecting QSAR descriptors. 

Multivariate models using neural networks, support vector machines and least median squares regression have been used to predict hERG activity [96-98]. These types of models function more as computational black box assays. 

Eden Prairie, MN), DICKEY-john OmegAnalyzerG (DICKEY-john Corp, Auburn, IL), Perten DA 7200 (Perten Instruments Inc., Springfield, IL), Bruker Optics/ Cog-nis QTA (Brucker Optics Inc., Billerica, MA), and an ASD LabSpec Pro (Analytical Spectral Devices Inc., Boulder, CO) for 18 amino acids. Partial least squares (PLS) and support vector machines (SVM) regression models performed significantly better than artificial neural networks (ANN). They used a calibration data set of 526 samples... 

For each sub-dataset pCsub, ysub)i, a sub-model can be constructed using a selected method, e.g. partial least squares (PLS) [11] or support vector machines (SVM) [12]. Denote the submodel established asfi (X). Then, all these sub-models can be put into a collection ... 

Harding, Popelier, and co-workers [285,286] have employed a variety of quantum chemical approaches in their estimation of the pK s ol oxyacids. In a study of 228 carboxylic acids they used what they call quantum chemical topology to find pK estimates. They tested several different methods, including partial least squares (PLS), support vector machines (SVMs), and radial basis function neural networks (RBFNNs) with Hartree-Fock and density functional calculations, concluding that the SVM models with HF/6-31G calculations were most efficient [285]. Foi a data set of 171 phenols they found that the C-0 bond length provided an effective descriptor for pK estimation [286]. 

Wang et al. [44] used an EN equipped with MOX sensors together with support vector machine (SVM) and partial least squares (PLS) to predict the total viable counts in chilled pork samples. The achieved correlation coefficients for training and validation were close to 90 %, which suggested that the EN system could be used as a simple and rapid technique for absolving the task. 

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